Abstract
In this paper we present a new system for segmenting non-rigid objects in moving camera sequences for indoor and outdoor scenarios that achieves a correct object segmentation via global MAP-MRF framework formulation for the foreground and background classification task. Our proposal, suitable for video indexation applications, receives as an input an initial segmentation of the object to segment and it consists of two region-based parametric probabilistic models to model the spatial (x,y) and color (r,g,b) domains of the foreground and background classes. Both classes rival each other in modeling the regions that appear within a dynamic region of interest that includes the foreground object to segment and also, the background regions that surrounds the object. The results presented in the paper show the correctness of the object segmentation, reducing false positive and false negative detections originated by the new background regions that appear near the region of the object.
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Wren, C.R., Azarbayejani, A., Darrell, T., Pentland, A.P.: Pfinder: Real-time tracking of the human body. IEEE Trans. on Pattern Analysis and Machine Intelligence 19(7), 780–785 (2002)
Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: IEEE Proc. Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 246–252 (1999)
Isard, M., Blake, A.: Condensation conditional density propagation for visual tracking. International Journal of Computer Vision 29(1), 5–28 (1998)
Cristani, M., Farenzena, M., Bloisi, D., Murino, V.: Background subtraction for automated multisensor surveillance: a comprehensive review. EURASIP Journal on Advances in Signal Processing 2010, Article ID 343057, 24 (2010)
Araki, S., Matsuoka, T., Yokoya, N., Takemura, H.: Real-time tracking of multiple moving object contours in a moving camera image sequence. IEICE Trans. on Information and Systems 83(7), 1583–1591 (2000)
Sawhney, H.S., Ayer, S.: Compact representations of videos through dominant and multiple motion estimation. IEEE Trans. on Pattern Analysis and Machine Intelligence 18(8), 814–830 (2002)
Jin, Y., Tao, L., Di, H., Rao, N.I., Xu, G.: Background modeling from a free-moving camera by multi-layer homography algorithm. In: Proc. IEEE International Conference on Image Processing, pp. 1572–1575 (2008)
Smith, S.M., Brady, J.M.: ASSET-2: Real-time motion segmentation and shape tracking. IEEE Trans. on Pattern Analysis and Machine Intelligence 17(8), 814–820 (2002)
Grinias, I., Tziritas, G.: A semi-automatic seeded region growing algorithm for video object localization and tracking. Signal Processing: Image Communication 16(10), 977–986 (2001)
Cucchiara, R., Prati, A., Vezzani, R.: Real-time motion segmentation from moving cameras. Real-Time Imaging 3(10), 127–143 (2004)
Leichter, I., Lindenbaum, M., Rivlin, E.: Bittracker, a bitmap tracker for visual tracking under very general conditions. IEEE Trans. on Pattern Analysis and Machine Intelligence 30(9), 1572–1588 (2008)
Yu, T., Zhang, C., Cohen, M., Rui, Y., Wu, Y.: Monocular video foreground/background segmentation by tracking spatial-color Gaussian mixture models. IEEE Workshop on Motion and Video Computing (2007)
Sheikh, Y., Shah, M.: Bayesian modeling of dynamic scenes for object detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(11), 1778–1792 (2005)
Gallego, J., Pardas, M., Haro, G.: Bayesian foreground segmentation and tracking using pixel-wise background model and region based foreground model. IEEE Int. Conf. on Image Processing, 3205–3208 (2009)
Gallego, J., Pardas, M.: Enhanced bayesian foreground segmentation using brightness and color distortion region-based model for shadow removal. In: IEEE Int. Conf. on Image Processing, pp. 3449–3452 (2010)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological) 39(1), 1–38 (1977)
Khan, S., Shah, M.: Tracking people in presence of occlusion. In: Asian Conf. on Computer Vision, vol. 5 (2000)
Fasano, G., Franceschini, A.: A multidimensional version of the Kolmogorov-Smirnov test. Royal Astronomical Society 225, 155–170 (2000); Monthly Notices (ISSN 0035-8711)
Kullback, S.: The kullback-leibler distance. The American Statistician 41, 340–341 (1987)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimisation via graph cuts. IEEE Trans. on Pattern Analysis and Machine Intelligence 29, 1222–1239 (2001)
Tiburzi, F., Escudero, M., Bescós, J., Martínez, J.M.: A Ground-truth for Motion-based Video-object Segmentation. In: IEEE Int. Conf. on Image Processing Workshop on Multimedia Information Retrieval: New Trends and Challenges, pp. 17–20 (2008)
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Gallego, J., Pardàs, M., Solano, M. (2012). Foreground Objects Segmentation for Moving Camera Scenarios Based on SCGMM. In: Salerno, E., Çetin, A.E., Salvetti, O. (eds) Computational Intelligence for Multimedia Understanding. MUSCLE 2011. Lecture Notes in Computer Science, vol 7252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32436-9_17
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DOI: https://doi.org/10.1007/978-3-642-32436-9_17
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